To evaluate our method we used several publicly available 3D datasets, with different characteristics.
The TOSCA [1]} dataset consists of 80 synthetic
meshes with 9 different deformable objects. TOSCA is a standard benchmark for the evaluation of simplification methods since it includes high-resolution meshes with a varying number of vertices between 10K to 50K. In addition, it is an excellent candidate to assess feature-preserving simplification, since most of its meshes are non-smooth consisting of high curvature regions. We split TOSCA dataset into 80% training and 20% testing, in a stratified manner. Additionally, we used the popular ModelNet10 dataset [2]} which was initially preprocessed to remove redundant points and retain only high-resolution meshes that have more than 2K vertices. For ModelNet10 we used the official train-test split. Finally, we assessed the performance of our method in a fixed topology setting, i.e. where all meshes have the same connectivity, as in MeIn3D face dataset [3]}, where the surfaces of which are significantly smoother than the previous use cases. This experiment aims to assess the ability of the proposed method to simplify fixed topology meshes by fine-tuning the model with only a few samples from the dataset. MeIn3D dataset consists of 10K meshes with 28431 vertices each, formed from distinct human faces. For MeIn3D experiments, we randomly selected 10% of the meshes for training and 90% for testing.
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